SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The near-term technology improvements which can reduce the fuel consumed in the JT9D, JT8D, and JT3D turbofans in commercial fleet operation through the 1980's are identified. Projected technology advances are identified and evaluated for new turbofans to be developed after 1985. Programs are recommended for developing the necessary technology.
Economic studies were conducted for three general fuel conserving options: (1) improving fuel consumption characteristics of existing aircraft via retrofit modifications; (2) introducing fuel efficient derivations of existing production aircraft and/or introducing fuel efficient, current state-of-the-art new aircraft; and (3) introducing an advanced state-of-the-art turboprop airplane. These studies were designed to produce an optimum airline fleet mix for the years 1980, 1985 and 1990. The fleet selected accommodated a normal growth market by introducing somewhat larger aircraft while solving for maximum departure frequencies and a minimum load factor corresponding to a 15% investment hurdle rate. Fuel burnt per available-seat-mile flown would drop 22% from 1980 to 1990 due to the use of more fuel efficient aircraft designs, larger average aircraft size, and increased seating density. An inflight survey was taken to determine air traveler attitudes towards a new generation of advanced turboprops.
The fuel saving and economic potentials of the prop-fan high-speed propeller concept were evaluated for twin-engine commercial transport airplanes designed for 3333.6 km range, 180 passengers, and Mach 0.8 cruise. A fuel saving of 9.7% at the design range was estimated for a prop-fan airplane having wing-mounted engines, while a 5.8% saving was estimated for a design having the engines mounted on the aft body. The fuel savings and cost were found to be sensitive to the propeller noise level and to aerodynamic drag effects due to wing-slipstream interaction. Uncertainties in these effects could change the fuel savings as much as + or - 50%. A modest improvement in direct operating cost (DOC) was estimated for the wing-mounted prop-fan at current fuel prices. This improvement could become substantial in the event of further relative increases in the price of oil. The improvement in DOC requires the achievement of the nominal fuel saving and reductions in propeller and gearbox maintenance costs relative to current experience.
For abstract, see N77-14029.
For abstract, see preceding accession.
The effectiveness and associated costs of operational and technical options for reduced fuel consumption by Douglas aircraft in the domestic airline fleet are assessed. Areas explored include alternative procedures for airline and flight operations, advanced and state of the art technology, modification and derivative configurations, new near-term aircraft, turboprop configuration studies, and optimum aircraft geometry. Data for each aircraft studied is presented in tables and graphs.
The impact of the most promising fuel conserving options on fuel consumption, passenger demand, operating costs, and airline profits when implemented into the U.S. domestic and international airline fleets is assessed. The potential fuel savings achievable in the U.S. scheduled air transportation system over the forecast period, 1973-1990, are estimated.
The fuel saving potential and cost effectiveness of numerous operational and technical options proposed for reducing the fuel consumption of the U.S. commercial airline fleet was examined and compared. The impact of the most promising fuel conserving options on fuel consumption, passenger demand, operating costs and airline profits when implemented in the U.S. domestic and international airline fleets was determined. A forecast estimate was made of the potential fuel savings achievable in the U.S. scheduled air transportation system. Specifically, the means for reducing the jet fuel consumption of the U.S. scheduled airlines in domestic and international passenger operations were investigated. A design analysis was made of two turboprop aircraft as possible fuel conserving derivatives of the DC-9-30.
The sodium and potassium metabolism was studied in a thermal chamber at 35 deg and 80 percent relative humidity in 8 men for a period of 6 days. The control group (3 subjects) were outside of the chamber at a comfortable ambient temperature. The intracellular sodium and potassium metabolism were assessed based on their content in the erythrocytes. The finding was that during adaptation to heat, a considerable amount of sodium was excreted by the body in the sweat and urine (about 1/3 of the sodium content of the human body) as compared with its intake and the amount of potassium retained in the body. Changes in the concentration of sodium and potassium may serve as indexes of the state of adaptation processes during constant exposure to heat.
This paper presents an overview of the propulsion research and technology portfolio of NASA Fundamental Aeronautics Program Fixed Wing Project. The research is aimed at significantly reducing the thrust specific fuel/energy consumption of notional advanced fixed wing aircraft (by 60 % relative to a baseline Boeing 737-800 aircraft with CFM56-7B engines) in the 2030-2035 time frame. The research investments described herein are aimed at improving propulsive efficiency through higher bypass ratio fans, improving thermal efficiency through compact high overall pressure ratio gas generators, and exploring the potential benefits of boundary layer ingestion propulsion and hybrid gas-electric propulsion concepts.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Energy currently used in grain production, the effect of ethanol production on agricultural energy consumption, energy credits for ethanol by-products, and land availability and the potential for obtaining ethanol from grain are discussed. Dry milling, wet milling, sensitivity analysis, potential for reduced energy consumption are also discussed.
In this paper a power conversion and energy consumption model for an exascale supercomputer is investigated. Power consumption, energy loss and efficiency are derived for the 27.2 MW liquid-cooled, centralized, High Performance Computing (HPC) power system, which is supplied directly from the 480 V three-phase mains. Two energy conversion stages are analyzed, measured and modeled. The model is developed in order to be adapted and implemented in a digital twin platform utilizing a Resource Allocator and Power Simulator (RAPS) module. RAPS enables estimation of potential energy savings in the direct AC power supply architecture via both conventional rectifier load sharing (commonly used in HPC systems), as well as smart rectifier load sharing. Moreover, besides the direct AC supply architecture analysis, the full direct DC supply architecture with with 1 kV DC bus were also studied. Comparison of 10 hour time frame operation of the system, with direct 480 V AC voltage supply with conventional and smart load sharing and medium dc voltage supply were done. For the direct AC supply architecture, with conventional and smart load sharing the predicted power loss was approximately 840 kW and 820 kW, respectively and the predicted total system efficiency was 92.87% and 93.05%, respectively. For the direct DC supply architecture with the 1000 V DC supply bus power loss was approximately 340 kW and the predicted total system efficiency was 97.02%.
Here, this study investigates the impact of ambient temperature on the range of electric vehicles (EVs) by analyzing its effects on usable battery energy (UBE), heating, ventilation, and air conditioning (HVAC) energy consumption, and powertrain energy losses. Chassis dynamometer tests within a thermal chamber were conducted under various temperature conditions to investigate these impacts. The results indicate that lower temperatures lead to a decrease in UBE for lithium-ion batteries in EVs. At −18 °C, the UBE exhibited reductions of 4---8 % compared to the UBE at 22 °C. Battery thermal management strategies significantly affected the UBE loss, with different strategies resulting in distinct UBE reductions. HVAC energy consumption, especially for interior heating, proved to be the most dominant variable affecting EV driving range. Larger discrepancies between the HVAC target temperature (22 °C) and the ambient temperature increased HVAC energy usage. The type of HVAC system also influenced energy consumption, where EVs equipped with heat pumps demonstrated lower energy consumption for heating compared to those relying solely on resistance heaters. Ambient temperature also influenced motor energy consumption due to increased frictions, powertrain losses and tire rolling resistance at lower temperatures; consequently, regenerative braking energy decreased in cold conditions. Combining these effects influenced the overall energy consumption and driving range of EVs. At −18 °C, the driving range saw a substantial decrease of up to 60 % compared to 22 °C, while a slight decrease was observed at 35 °C.
Modern GPU-rich HPC systems are increasingly becoming energy-constrained. Thus, understanding an application’s energy consumption becomes essential. Unfortunately, current GPU energy attribution techniques are either inaccurate, inflexible, or outdated. Therefore, we propose Wattchmen, a flexible methodology for measuring, attributing, and predicting GPU energy consumption. We construct a per-instruction energy model using a diverse set of microbenchmarks to systematically quantify the energy consumption of GPU instructions, enabling finer-grain prediction and energy consumption breakdowns for applications. Compared with the state-of-the-art systems like AccelWattch (32%) and Guser (25%), across 16 popular GPGPU, graph analytics, HPC, and ML workloads, Wattchmen reduces the mean absolute percent error (MAPE) to 14% on V100 GPUs. Furthermore, we show that Wattchmen provides similar MAPEs for water-cooled V100s (15%) and extends to later architectures, including air-cooled A100 (11%) and H100 (12%) GPUs. Finally, to further demonstrate Wattchmen ’s value, we apply it to applications such as Backprop and QMCPACK, where Wattchmen ’s insights enable energy reductions of up to 35%.
Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.
Buildings make up a large portion of energy consumption in the U.S. today. Understanding their energy consumption patterns can improve their efficiency, but requires detailed models that rely on incomplete or unknown information. Previous work has shown that artificial intelligence (AI) can be used to predict missing information and even suggest upgrades to improve building efficiency. However, building upgrades may require undesirable upfront costs. Oppositely, advanced control could improve building efficiency with negligible upfront cost. To explore the tradeoffs between these two approaches, in this work we propose a workflow to compute optimal temperature setpoint schedules to minimize energy consumption and operational cost. Results show that modifying the temperature setpoints in a building using model predictive control (MPC) can effectively reduce its energy consumption and operational cost. This optimal operation cannot fully meet a desired goal. However, we show that by considering MPC in addition to component upgrades, a desired goal can be met with significantly less upfront costs.